DTAO和BITTENSOR的演變：以市場為導向的激勵措施重塑分散的AI

該分析研究了Bittensor的動態TAO（DTAO）升級如何解決分散的AI中固有的挑戰，從而將網絡定位為該新興部門的開創性力量。

In the rapidly evolving landscape of artificial intelligence, the focus has shifted from foundational model development to the optimization of existing systems, a trend evident in the contributions of industry leaders such as DeepSeek and OpenAI. This transition is closely tied to the introduction of Dynamic TAO (dTAO) by Bittensor, a move that has far-reaching implications for decentralized AI.

在人工智能的快速發展的景觀中，重點已從基礎模型開發轉變為現有系統的優化，這在諸如DeepSeek和Openai等行業領導者的貢獻中很明顯。這種過渡與Bittensor引入動態Tao（DTAO）緊密相關，該舉動對分散的AI具有深遠的影響。

This analysis delves into how dTAO addresses inherent challenges within decentralized AI, positioning the network as a pioneering force in this emerging sector.

該分析研究了DTAO如何應對分散的AI內的固有挑戰，將網絡定位為該新興部門的開拓力量。

Bittensor’s Architecture: A Framework for Decentralized AI

Bittensor的體系結構：分散AI的框架

Bittensor’s architecture is composed of three key elements: the Subtensor blockchain, a Polkadot parachain with EVM compatibility; 64 specialized subnets; and a governance-focused Root Subnet. The network employs a dual-key security system, Coldkey-Hotkey, and a subnet UID framework to facilitate secure and open participation for miners and validators.

Bittensor的體系結構由三個關鍵要素組成：微調區塊鏈，這是具有EVM兼容性的Polkadot Parachain； 64個專業子網；和一個以治理為重點的根子網。該網絡採用雙鍵安全系統，ColdKey-Hotkey和子網UID框架，以促進礦工和驗證者的安全和開放參與。

At the heart of its operational model is the Yuma Consensus (YC), a dynamic incentive mechanism that diverges from traditional static reward systems. YC assesses validators’ weight vectors, derived from historical performance and stake, to distribute TAO rewards every 12 seconds, establishing a self-regulating “stake → weight → reward” loop. This mechanism aligns contributions with incentives while mitigating malicious activities through continuous adjustments.

其運營模型的核心是YUMA共識（YC），這是一種與傳統靜態獎勵系統不同的動態激勵機制。 YC評估驗證者的重量向量，該體重向量源自歷史表現和股份，以每12秒分發一次陶獎，並建立自我調節的“股份→權重→獎勵”環路。這種機制將貢獻與激勵措施保持一致，同時通過持續調整來減輕惡意活動。

The dTao Upgrade: Shifting to Market-Driven Resources

DTAO升級：轉移到市場驅動的資源

The dTao upgrade, implemented on February 13, 2025, introduces liquidity pools for subnet tokens, fundamentally altering Bittensor’s economic framework. Key innovations include:

DTAO升級於2025年2月13日實施，引入了子網令牌的流動性池，從根本上改變了Bittensor的經濟框架。關鍵創新包括：

* Creation of a common liquidity pool for all subnet tokens on Subtensor.

*為子觀念上的所有子網令牌創建一個常見的流動性池。

* Adjustment of the YC to factor in subnet token prices in addition to validators’ performance.

*調整YC以外，除了驗證者的性能外，要考慮子網令牌價格。

* Introduction of a subnet economic performance ranking system based on metrics like token price and liquidity.

*基於標記價格和流動性等指標的子網經濟績效排名系統的引入。

* Adjustment of TAO emissions to favor subnets with better market performance and higher user engagement.

*調整TAO排放，以偏愛具有更好的市場性能和更高用戶參與度的子網。

This upgrade addresses previous systemic limitations, such as validator centralization, resource redundancy, and misaligned incentives. By linking subnet rewards to market performance, dTao fosters competition, encouraging the development of specialized AI solutions, ranging from multimodal content detection to decentralized search engines.

此升級解決了以前的系統限制，例如驗證者集中化，資源冗餘和未對準激勵措施。通過將子網獎勵與市場績效聯繫起來，DTAO促進了競爭，鼓勵了專門的AI解決方案的開發，從多模式內容檢測到分散搜索引擎。

Ecosystem Impact: High-Performance Subnets Emerge

生態系統影響：高性能子網出現

The implementation of dTao has led to the emergence of high-performing subnets, operating within a self-reinforcing feedback loop where increasing token prices attract greater TAO emissions, subsequently drawing more users and validators. Examples include:

DTAO的實施導致了高性能子網的出現，該子網在自我增強的反饋迴路中運行，在這種反饋循環中，代價上漲的價格上漲吸引了更大的TAO排放，隨後吸引了更多的用戶和驗證者。示例包括：

* **Prado**: Focused on multi-modal content detection, Prado has witnessed significant user growth due to the integration of several AI-powered services, resulting in high levels of on-chain activity and a rising token price.

*** Prado **：專注於多模式內容檢測，Prado由於整合了多種AI驅動的服務而見證了用戶的顯著增長，從而導致高水平的鏈上活動和代幣價格上漲。

* As the primary subnet for decentralized search, Kaito has attracted a large user base, further boosting its token. However, despite technical capabilities, the lack of integration with core product utility has led to limited user engagement and a stagnating token price, highlighting the importance of balancing technical proficiency with market responsiveness.

*作為分散搜索的主要子網，Kaito吸引了大型用戶群，進一步提高了其令牌。然而，儘管技術能力，但與核心產品實用程序缺乏集成導致用戶參與度有限和代幣價格停滯不前，這強調了平衡技術水平與市場響應能力的重要性。

Despite the advancements introduced by dTao, HTX Research also identifies ongoing challenges, including the lack of real-world demand drivers for TAO rewards, the potential for resource redundancy among overlapping subnets, and persistent validator centralization.

儘管DTAO提出了進步，但HTX研究還確定了持續的挑戰，包括缺乏TAO獎勵的現實世界需求驅動因素，重疊子網中資源冗餘的潛力以及持續的驗證者集中化。

To ensure sustained growth, HTX Research emphasizes the necessity for on-chain verifiability, standardized subnet performance benchmarking systems, and the integration of subnet token utility, such as governance or service access, to reduce speculative trading.

為了確保持續增長，HTX研究強調了鏈核能，標準化子網性能基準制度的必要性以及子網令牌效用（例如治理或服務訪問）的整合，以減少投機性交易。

Conclusion

結論

Bittensor’s dTao upgrade marks a departure from centralized governance models and introduces a system of market-driven incentives. While challenges remain in achieving optimal resource allocation and sustained user engagement, Bittensor’s architecture and economic model provide a unique framework for decentralized AI.

Bittensor的DTAO升級標誌著與集中式治理模型背道而馳，並引入了市場驅動的激勵措施。儘管在實現最佳資源分配和持續的用戶參與方面仍然存在挑戰，但Bittensor的體系結構和經濟模型為分散的AI提供了獨特的框架。

As subnet tokens evolve into tools with tangible utility, Bittensor is well-positioned to reshape the competitive and collaborative dynamics within AI ecosystems.

隨著子網代幣發展為具有切實實用程序的工具，Bittensor的位置良好，可以重塑AI生態系統中的競爭和協作動態。

HTX Research will continue to closely examine these developments and offer actionable insights into the intersection of AI and blockchain technology.

HTX研究將繼續密切檢查這些發展，並為AI和區塊鏈技術的交集提供可行的見解。